Sparse neural networks, where a substantial portion of the components are eliminated, have widely shown their versatility in model compression, robustness improvement, and overfitting mitigation. However, traditional methods for obtaining such sparse networks usually involve a fully pre-trained, dense model. As foundation models become prevailing, the cost of this pre-training step can be prohibitive. On the other hand, training intrinsic sparse neural networks from scratch usually leads to inferior performance compared to their dense counterpart. 

In this talk, I will present a series of approaches to obtain such fantastic sparse neural networks by training from scratch without the need for any dense pre-training steps, including dynamic sparse training, static sparse with random pruning, and only masking no training. First, I will introduce the concept of in-time over-parameterization (ITOP) (ICML2021) which enables training sparse neural networks from scratch (commonly known as sparse training) to attain the full accuracy of dense models. By dynamically exploring new sparse topologies during training, we avoid the costly necessity of pre-training and re-training, requiring only a single training run to obtain strong sparse neural networks. Secondly, ITOP involves additional overhead due to the frequent change in sparse topology. Our following work (ICLR2022) demonstrates that even a naïve, static sparse network produced by random pruning can be trained to achieve dense model performance as long as our model is relatively larger. Moreover, I will further discuss that we can continue to push the extreme of training efficiency by only learning masks at initialization without any weight updates, addressing the over-smoothing challenge in building deep graph neural networks (LoG2022).

The tremendous success of the Machine Learning paradigm heavily relies on the development of powerful optimization methods, and the canonical algorithm for training learning models is SGD (Stochastic Gradient Descent). Nevertheless, the latter is quite different from Gradient Descent (GD) which is its noiseless counterpart. Concretely, SGD requires a careful choice of the learning rate, which relies on the properties of the noise as well as the quality of initialization.

 It further requires the use of a test set to estimate the generalization error throughout its run. In this talk, we will present a new SGD variant that obtains the same optimal rates as SGD, while using noiseless machinery as in GD. Concretely, it enables to use the same fixed learning rate as GD and does not require to employ a test/validation set. Curiously, our results rely on a novel gradient estimate that combines two recent mechanisms which are related to the notion of momentum.

Finally, as much as time permits, I will discuss several applications where our method can be extended.

Learning to process and analyze the raw weight matrices of neural networks is an emerging research area with intriguing potential applications like editing and analyzing Implicit Neural Representations (INRs), weight pruning/quantization, and function editing. However, weight spaces have inherent permutation symmetries – permutations can be applied to the weights of an architecture, yielding new weights that represent the same function. As with other data types like graphs and point clouds, these symmetries make learning in weight spaces challenging.

This talk will overview recent advances in designing architectures that can effectively operate on weight spaces while respecting their underlying symmetries. First, we will discuss our ICML 2023 paper which introduces novel equivariant architectures for learning on multilayer perceptron weight spaces. We first characterize all linear equivariant layers for their symmetries and then construct networks composed of these layers. We then turn to our ICLR 2024 work, which generalizes the approach to diverse network architectures using what we term Graph Metanetworks (GMN). This is done by representing input networks as graphs and processing them with graph neural networks. We show the resulting metanetworks are expressive and equivariant to weight space symmetries of the architecture being processed. Our graph metanetworks are applicable to CNNs, attention layers, normalization layers, and more. Together, these works make promising steps toward versatile and principled architectures for weight-space learning.